nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
The nbeerbower/Qwen2.5-Gutenberg-Doppel-14B is a 14.8 billion parameter Qwen2.5-14B-Instruct base model, fine-tuned by nbeerbower using ORPO on Gutenberg-derived datasets. This model specializes in text generation, demonstrating strong performance on instruction following (IFEval) and general reasoning (BBH) tasks. It is designed for applications requiring robust language understanding and generation across multiple languages, including English, Chinese, French, and more.
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Qwen2.5-Gutenberg-Doppel-14B Overview
This model is a 14.8 billion parameter language model developed by nbeerbower, based on the Qwen2.5-14B-Instruct architecture. It has been fine-tuned using the ORPO (Optimized Reward Policy Optimization) method over three epochs, leveraging two distinct Gutenberg-derived datasets: jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo. This specialized training aims to enhance its text generation capabilities and instruction following.
Key Capabilities & Performance
The model demonstrates notable performance across several benchmarks, as evaluated on the Open LLM Leaderboard:
- IFEval (0-Shot): Achieved a strict accuracy of 80.91%, indicating strong instruction-following ability.
- BBH (3-Shot): Scored 48.24% normalized accuracy on Big-Bench Hard, reflecting its general reasoning skills.
- MMLU-PRO (5-shot): Recorded 43.57% accuracy, showcasing its performance on advanced multi-task language understanding.
It supports a wide array of languages, including English, Chinese, French, Spanish, German, and more, making it suitable for multilingual applications.
Good For
- Instruction Following: Excels in tasks requiring precise adherence to given instructions.
- General Text Generation: Suitable for generating coherent and contextually relevant text.
- Multilingual Applications: Its broad language support makes it versatile for global use cases.
- Research and Development: Provides a strong base for further fine-tuning or experimentation, particularly for tasks benefiting from its Gutenberg-derived training.